Overview

Dataset statistics

Number of variables36
Number of observations1033
Missing cells506
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.7 KiB
Average record size in memory288.1 B

Variable types

Categorical9
Boolean14
Numeric13

Alerts

GPA year 1 is highly correlated with Overall GPAHigh correlation
GPA year 2 is highly correlated with Overall GPAHigh correlation
GPA year 3 is highly correlated with Overall GPA and 2 other fieldsHigh correlation
Overall GPA is highly correlated with GPA year 1 and 4 other fieldsHigh correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with GPA year 3 and 2 other fieldsHigh correlation
Pass is highly correlated with GPA year 3 and 2 other fieldsHigh correlation
English is highly correlated with MathsHigh correlation
Maths is highly correlated with EnglishHigh correlation
GPA year 1 is highly correlated with Overall GPAHigh correlation
GPA year 2 is highly correlated with GPA year 3 and 1 other fieldsHigh correlation
GPA year 3 is highly correlated with GPA year 2 and 1 other fieldsHigh correlation
Overall GPA is highly correlated with GPA year 1 and 4 other fieldsHigh correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with Overall GPA and 1 other fieldsHigh correlation
Pass is highly correlated with Overall GPA and 1 other fieldsHigh correlation
GPA year 1 is highly correlated with Overall GPAHigh correlation
Overall GPA is highly correlated with GPA year 1High correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with PassHigh correlation
Pass is highly correlated with FailsHigh correlation
UCAS is highly correlated with EthnicityHigh correlation
Progress is highly correlated with desertionHigh correlation
Polar 4 Score is highly correlated with BursaryHigh correlation
Bursary is highly correlated with Polar 4 ScoreHigh correlation
Ethnicity is highly correlated with UCASHigh correlation
A Levels is highly correlated with BtecHigh correlation
desertion is highly correlated with ProgressHigh correlation
Btec is highly correlated with A LevelsHigh correlation
Studenot Visa is highly correlated with BritishHigh correlation
British is highly correlated with Studenot VisaHigh correlation
Course is highly correlated with UCAS and 1 other fieldsHigh correlation
UCAS is highly correlated with Course and 2 other fieldsHigh correlation
25 Above is highly correlated with EthnicityHigh correlation
Disabilityes is highly correlated with BursaryHigh correlation
Ethnicity is highly correlated with Course and 2 other fieldsHigh correlation
desertion is highly correlated with UCAS and 9 other fieldsHigh correlation
British is highly correlated with English native Language and 1 other fieldsHigh correlation
English native Language is highly correlated with BritishHigh correlation
SLC is highly correlated with Studenot VisaHigh correlation
Care Leaver is highly correlated with RefugeeHigh correlation
Studenot Visa is highly correlated with SLCHigh correlation
Refugee is highly correlated with Care LeaverHigh correlation
Lonodono Permanoenot Residenoce is highly correlated with BritishHigh correlation
UCAS Points is highly correlated with English and 1 other fieldsHigh correlation
English is highly correlated with UCAS Points and 1 other fieldsHigh correlation
Maths is highly correlated with UCAS Points and 1 other fieldsHigh correlation
A Levels is highly correlated with BtecHigh correlation
Btec is highly correlated with A LevelsHigh correlation
Bursary is highly correlated with DisabilityesHigh correlation
Attendance is highly correlated with desertion and 2 other fieldsHigh correlation
GPA year 1 is highly correlated with desertion and 2 other fieldsHigh correlation
GPA year 2 is highly correlated with desertion and 3 other fieldsHigh correlation
GPA year 3 is highly correlated with desertion and 4 other fieldsHigh correlation
Overall GPA is highly correlated with desertion and 7 other fieldsHigh correlation
Progress is highly correlated with desertion and 7 other fieldsHigh correlation
First Sit is highly correlated with desertion and 2 other fieldsHigh correlation
Second Sit is highly correlated with First Sit and 1 other fieldsHigh correlation
Fails is highly correlated with desertion and 3 other fieldsHigh correlation
No Submissions is highly correlated with First Sit and 2 other fieldsHigh correlation
Pass is highly correlated with desertion and 3 other fieldsHigh correlation
Re Takes is highly correlated with GPA year 3 and 1 other fieldsHigh correlation
Polar 4 Score has 88 (8.5%) missing values Missing
UCAS Points has 54 (5.2%) missing values Missing
English has 160 (15.5%) missing values Missing
Maths has 161 (15.6%) missing values Missing
GPA year 2 has 112 (10.8%) zeros Zeros
GPA year 3 has 276 (26.7%) zeros Zeros
Second Sit has 208 (20.1%) zeros Zeros
Fails has 848 (82.1%) zeros Zeros
No Submissions has 423 (40.9%) zeros Zeros

Reproduction

Analysis started2022-08-04 14:41:28.564091
Analysis finished2022-08-04 14:42:08.908497
Duration40.34 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Course
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)1.3%
Missing2
Missing (%)0.2%
Memory size8.2 KiB
BA
541 
ba
380 
BA Business Management
 
32
BA Business Management Enterpreneurship and Innovation
 
24
BA Business Management International Business
 
12
Other values (8)
 
42

Length

Max length55
Median length2
Mean length4.929194956
Min length2

Characters and Unicode

Total characters5082
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowBA Business Manangement Enterpreneurship and Innovation
2nd rowBA Business Management
3rd rowBA Business Management Enterpreneurship and Innovation
4th rowBA Business Management
5th rowBA Business Management Enterpreneurship and Innovation

Common Values

ValueCountFrequency (%)
BA541
52.4%
ba380
36.8%
BA Business Management32
 
3.1%
BA Business Management Enterpreneurship and Innovation24
 
2.3%
BA Business Management International Business12
 
1.2%
MBA10
 
1.0%
Ba Business Management Finance9
 
0.9%
BA 9
 
0.9%
BA Business Management Marketing4
 
0.4%
Ba4
 
0.4%
Other values (3)6
 
0.6%

Length

2022-08-04T15:42:09.065016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ba1020
77.1%
business98
 
7.4%
management83
 
6.3%
enterpreneurship27
 
2.0%
and27
 
2.0%
innovation27
 
2.0%
international12
 
0.9%
mba11
 
0.8%
finance9
 
0.7%
marketing6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
B749
14.7%
a658
12.9%
A638
12.6%
n495
9.7%
b380
7.5%
e378
7.4%
s321
 
6.3%
302
 
5.9%
i179
 
3.5%
t170
 
3.3%
Other values (18)812
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3211
63.2%
Uppercase Letter1565
30.8%
Space Separator302
 
5.9%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a658
20.5%
n495
15.4%
b380
11.8%
e378
11.8%
s321
10.0%
i179
 
5.6%
t170
 
5.3%
u125
 
3.9%
r99
 
3.1%
g92
 
2.9%
Other values (9)314
9.8%
Uppercase Letter
ValueCountFrequency (%)
B749
47.9%
A638
40.8%
M103
 
6.6%
I39
 
2.5%
E27
 
1.7%
F9
 
0.6%
Space Separator
ValueCountFrequency (%)
302
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4776
94.0%
Common306
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B749
15.7%
a658
13.8%
A638
13.4%
n495
10.4%
b380
8.0%
e378
7.9%
s321
6.7%
i179
 
3.7%
t170
 
3.6%
u125
 
2.6%
Other values (15)683
14.3%
Common
ValueCountFrequency (%)
302
98.7%
(2
 
0.7%
)2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B749
14.7%
a658
12.9%
A638
12.6%
n495
9.7%
b380
7.5%
e378
7.4%
s321
 
6.3%
302
 
5.9%
i179
 
3.5%
t170
 
3.3%
Other values (18)812
16.0%

UCAS
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
938 
False
95 
ValueCountFrequency (%)
True938
90.8%
False95
 
9.2%
2022-08-04T15:42:09.236832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

25 Above
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
no
868 
yes
161 
no
 
4

Length

Max length3
Median length2
Mean length2.159728945
Min length2

Characters and Unicode

Total characters2231
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no868
84.0%
yes161
 
15.6%
no 4
 
0.4%

Length

2022-08-04T15:42:09.368801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:09.517907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no872
84.4%
yes161
 
15.6%

Most occurring characters

ValueCountFrequency (%)
n872
39.1%
o872
39.1%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2227
99.8%
Space Separator4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n872
39.2%
o872
39.2%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2227
99.8%
Common4
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n872
39.2%
o872
39.2%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n872
39.1%
o872
39.1%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
4
 
0.2%

Disabilityes
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
967 
True
 
66
ValueCountFrequency (%)
False967
93.6%
True66
 
6.4%
2022-08-04T15:42:09.654097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Ethnicity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
White
495 
Asian
244 
Black/Black British African
159 
Other
73 
Unknown
 
13
Other values (13)
 
49

Length

Max length30
Median length5
Mean length9.176185866
Min length5

Characters and Unicode

Total characters9479
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsian British
2nd rowWhite
3rd rowAsian British Pakistani
4th rowWhite
5th rowAsian/Asian British

Common Values

ValueCountFrequency (%)
White495
47.9%
Asian244
23.6%
Black/Black British African159
 
15.4%
Other73
 
7.1%
Unknown13
 
1.3%
white6
 
0.6%
Asian/Asian British6
 
0.6%
Asian/ Asian British- Indian6
 
0.6%
Other Asian Background5
 
0.5%
Asian/Asian British- Indian4
 
0.4%
Other values (8)22
 
2.1%

Length

2022-08-04T15:42:09.789752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white503
35.1%
asian269
18.8%
british187
 
13.0%
black/black159
 
11.1%
african159
 
11.1%
other84
 
5.9%
asian/asian18
 
1.3%
unknown13
 
0.9%
indian13
 
0.9%
background11
 
0.8%
Other values (6)18
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i1377
14.5%
a825
 
8.7%
t785
 
8.3%
h779
 
8.2%
e592
 
6.2%
n555
 
5.9%
B518
 
5.5%
s500
 
5.3%
W497
 
5.2%
c494
 
5.2%
Other values (20)2557
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7281
76.8%
Uppercase Letter1599
 
16.9%
Space Separator401
 
4.2%
Other Punctuation183
 
1.9%
Dash Punctuation15
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1377
18.9%
a825
11.3%
t785
10.8%
h779
10.7%
e592
8.1%
n555
7.6%
s500
 
6.9%
c494
 
6.8%
r443
 
6.1%
k351
 
4.8%
Other values (9)580
8.0%
Uppercase Letter
ValueCountFrequency (%)
B518
32.4%
W497
31.1%
A464
29.0%
O84
 
5.3%
I15
 
0.9%
U13
 
0.8%
P6
 
0.4%
M2
 
0.1%
Space Separator
ValueCountFrequency (%)
401
100.0%
Other Punctuation
ValueCountFrequency (%)
/183
100.0%
Dash Punctuation
ValueCountFrequency (%)
-15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8880
93.7%
Common599
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1377
15.5%
a825
9.3%
t785
8.8%
h779
 
8.8%
e592
 
6.7%
n555
 
6.2%
B518
 
5.8%
s500
 
5.6%
W497
 
5.6%
c494
 
5.6%
Other values (17)1958
22.0%
Common
ValueCountFrequency (%)
401
66.9%
/183
30.6%
-15
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1377
14.5%
a825
 
8.7%
t785
 
8.3%
h779
 
8.2%
e592
 
6.2%
n555
 
5.9%
B518
 
5.5%
s500
 
5.3%
W497
 
5.2%
c494
 
5.2%
Other values (20)2557
27.0%

Gender
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Male
636 
Female
391 
Female
 
3
Male
 
3

Length

Max length7
Median length4
Mean length4.768635044
Min length4

Characters and Unicode

Total characters4926
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male636
61.6%
Female391
37.9%
Female 3
 
0.3%
Male 3
 
0.3%

Length

2022-08-04T15:42:09.952670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:10.130884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male639
61.9%
female394
38.1%

Most occurring characters

ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3887
78.9%
Uppercase Letter1033
 
21.0%
Space Separator6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1427
36.7%
a1033
26.6%
l1033
26.6%
m394
 
10.1%
Uppercase Letter
ValueCountFrequency (%)
M639
61.9%
F394
38.1%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4920
99.9%
Common6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
6
 
0.1%

desertion
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
874 
True
159 
ValueCountFrequency (%)
False874
84.6%
True159
 
15.4%
2022-08-04T15:42:10.274179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

British
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
650 
False
383 
ValueCountFrequency (%)
True650
62.9%
False383
37.1%
2022-08-04T15:42:10.404413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

English native Language
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
566 
False
467 
ValueCountFrequency (%)
True566
54.8%
False467
45.2%
2022-08-04T15:42:10.539982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
572 
True
461 
ValueCountFrequency (%)
False572
55.4%
True461
44.6%
2022-08-04T15:42:10.671023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Polar 4 Score
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.5%
Missing88
Missing (%)8.5%
Memory size8.2 KiB
4.0
299 
3.0
223 
5.0
167 
2.0
138 
1.0
118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2835
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0299
28.9%
3.0223
21.6%
5.0167
16.2%
2.0138
13.4%
1.0118
 
11.4%
(Missing)88
 
8.5%

Length

2022-08-04T15:42:10.797955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:10.953887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0299
31.6%
3.0223
23.6%
5.0167
17.7%
2.0138
14.6%
1.0118
 
12.5%

Most occurring characters

ValueCountFrequency (%)
.945
33.3%
0945
33.3%
4299
 
10.5%
3223
 
7.9%
5167
 
5.9%
2138
 
4.9%
1118
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1890
66.7%
Other Punctuation945
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0945
50.0%
4299
 
15.8%
3223
 
11.8%
5167
 
8.8%
2138
 
7.3%
1118
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.945
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.945
33.3%
0945
33.3%
4299
 
10.5%
3223
 
7.9%
5167
 
5.9%
2138
 
4.9%
1118
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.945
33.3%
0945
33.3%
4299
 
10.5%
3223
 
7.9%
5167
 
5.9%
2138
 
4.9%
1118
 
4.2%

SLC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
yes
734 
no
287 
no
 
12

Length

Max length3
Median length3
Mean length2.722168441
Min length2

Characters and Unicode

Total characters2812
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes734
71.1%
no287
 
27.8%
no 12
 
1.2%

Length

2022-08-04T15:42:11.123922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:11.282990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes734
71.1%
no299
28.9%

Most occurring characters

ValueCountFrequency (%)
y734
26.1%
e734
26.1%
s734
26.1%
n299
10.6%
o299
10.6%
12
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2800
99.6%
Space Separator12
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y734
26.2%
e734
26.2%
s734
26.2%
n299
10.7%
o299
10.7%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2800
99.6%
Common12
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
y734
26.2%
e734
26.2%
s734
26.2%
n299
10.7%
o299
10.7%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y734
26.1%
e734
26.1%
s734
26.1%
n299
10.6%
o299
10.6%
12
 
0.4%

Care Leaver
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing2
Missing (%)0.2%
Memory size8.2 KiB
no
980 
no
 
26
yes
 
19
no
 
6

Length

Max length3
Median length2
Mean length2.049466537
Min length2

Characters and Unicode

Total characters2113
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no980
94.9%
no 26
 
2.5%
yes19
 
1.8%
no6
 
0.6%
(Missing)2
 
0.2%

Length

2022-08-04T15:42:11.419995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:11.576941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no1012
98.2%
yes19
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n1012
47.9%
o1012
47.9%
32
 
1.5%
y19
 
0.9%
e19
 
0.9%
s19
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2081
98.5%
Space Separator32
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1012
48.6%
o1012
48.6%
y19
 
0.9%
e19
 
0.9%
s19
 
0.9%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2081
98.5%
Common32
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1012
48.6%
o1012
48.6%
y19
 
0.9%
e19
 
0.9%
s19
 
0.9%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1012
47.9%
o1012
47.9%
32
 
1.5%
y19
 
0.9%
e19
 
0.9%
s19
 
0.9%

Studenot Visa
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing3
Missing (%)0.3%
Memory size8.2 KiB
no
858 
yes
155 
no
 
17

Length

Max length3
Median length2
Mean length2.166990291
Min length2

Characters and Unicode

Total characters2232
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no858
83.1%
yes155
 
15.0%
no 17
 
1.6%
(Missing)3
 
0.3%

Length

2022-08-04T15:42:11.708764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:11.859752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no875
85.0%
yes155
 
15.0%

Most occurring characters

ValueCountFrequency (%)
n875
39.2%
o875
39.2%
y155
 
6.9%
e155
 
6.9%
s155
 
6.9%
17
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2215
99.2%
Space Separator17
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n875
39.5%
o875
39.5%
y155
 
7.0%
e155
 
7.0%
s155
 
7.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2215
99.2%
Common17
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n875
39.5%
o875
39.5%
y155
 
7.0%
e155
 
7.0%
s155
 
7.0%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n875
39.2%
o875
39.2%
y155
 
6.9%
e155
 
6.9%
s155
 
6.9%
17
 
0.8%

Refugee
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.7%
Memory size2.1 KiB
False
1002 
True
 
24
(Missing)
 
7
ValueCountFrequency (%)
False1002
97.0%
True24
 
2.3%
(Missing)7
 
0.7%
2022-08-04T15:42:11.988887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Lonodono Permanoenot Residenoce
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing5
Missing (%)0.5%
Memory size2.1 KiB
True
568 
False
460 
(Missing)
 
5
ValueCountFrequency (%)
True568
55.0%
False460
44.5%
(Missing)5
 
0.5%
2022-08-04T15:42:12.128987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

UCAS Points
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)6.1%
Missing54
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean109.113381
Minimum72
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:12.310117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile82
Q196
median104
Q3120
95-th percentile152
Maximum168
Range96
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.20580245
Coefficient of variation (CV)0.1851817098
Kurtosis0.8416214006
Mean109.113381
Median Absolute Deviation (MAD)11
Skewness0.9814397744
Sum106822
Variance408.2744527
MonotonicityNot monotonic
2022-08-04T15:42:12.508904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9684
 
8.1%
10451
 
4.9%
12847
 
4.5%
8036
 
3.5%
12036
 
3.5%
11235
 
3.4%
8435
 
3.4%
8833
 
3.2%
10033
 
3.2%
10330
 
2.9%
Other values (50)559
54.1%
(Missing)54
 
5.2%
ValueCountFrequency (%)
724
 
0.4%
8036
3.5%
8222
2.1%
8435
3.4%
851
 
0.1%
8610
 
1.0%
875
 
0.5%
8833
3.2%
897
 
0.7%
906
 
0.6%
ValueCountFrequency (%)
16825
2.4%
1625
 
0.5%
1608
 
0.8%
1551
 
0.1%
1538
 
0.8%
15215
1.5%
1486
 
0.6%
1464
 
0.4%
14418
1.7%
1366
 
0.6%

English
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.9%
Missing160
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean4.924398625
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:12.672811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.385131692
Coefficient of variation (CV)0.2812793596
Kurtosis0.3617214903
Mean4.924398625
Median Absolute Deviation (MAD)1
Skewness0.7507784704
Sum4299
Variance1.918589804
MonotonicityNot monotonic
2022-08-04T15:42:12.806075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4290
28.1%
5256
24.8%
6120
11.6%
382
 
7.9%
853
 
5.1%
751
 
4.9%
211
 
1.1%
910
 
1.0%
(Missing)160
15.5%
ValueCountFrequency (%)
211
 
1.1%
382
 
7.9%
4290
28.1%
5256
24.8%
6120
11.6%
751
 
4.9%
853
 
5.1%
910
 
1.0%
ValueCountFrequency (%)
910
 
1.0%
853
 
5.1%
751
 
4.9%
6120
11.6%
5256
24.8%
4290
28.1%
382
 
7.9%
211
 
1.1%

Maths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.9%
Missing161
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean4.774082569
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:12.946072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q35
95-th percentile7
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.19916459
Coefficient of variation (CV)0.2511822057
Kurtosis0.761532845
Mean4.774082569
Median Absolute Deviation (MAD)1
Skewness0.569859444
Sum4163
Variance1.437995713
MonotonicityNot monotonic
2022-08-04T15:42:13.243457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4345
33.4%
5256
24.8%
6124
 
12.0%
760
 
5.8%
346
 
4.5%
222
 
2.1%
814
 
1.4%
95
 
0.5%
(Missing)161
15.6%
ValueCountFrequency (%)
222
 
2.1%
346
 
4.5%
4345
33.4%
5256
24.8%
6124
 
12.0%
760
 
5.8%
814
 
1.4%
95
 
0.5%
ValueCountFrequency (%)
95
 
0.5%
814
 
1.4%
760
 
5.8%
6124
 
12.0%
5256
24.8%
4345
33.4%
346
 
4.5%
222
 
2.1%

A Levels
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.3%
Memory size2.1 KiB
True
576 
False
454 
(Missing)
 
3
ValueCountFrequency (%)
True576
55.8%
False454
43.9%
(Missing)3
 
0.3%
2022-08-04T15:42:13.395844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Btec
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
654 
True
379 
ValueCountFrequency (%)
False654
63.3%
True379
36.7%
2022-08-04T15:42:13.521971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.3%
Memory size2.1 KiB
False
527 
True
503 
(Missing)
 
3
ValueCountFrequency (%)
False527
51.0%
True503
48.7%
(Missing)3
 
0.3%
2022-08-04T15:42:13.647946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Bursary
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
788 
True
245 
ValueCountFrequency (%)
False788
76.3%
True245
 
23.7%
2022-08-04T15:42:13.780960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Attendance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)6.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean75.07751938
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:13.932828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile46
Q164
median76
Q388
95-th percentile97
Maximum100
Range80
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.7430199
Coefficient of variation (CV)0.2096901979
Kurtosis-0.6289405519
Mean75.07751938
Median Absolute Deviation (MAD)12
Skewness-0.395967175
Sum77480
Variance247.8426755
MonotonicityNot monotonic
2022-08-04T15:42:14.145859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6034
 
3.3%
9231
 
3.0%
9529
 
2.8%
7428
 
2.7%
9627
 
2.6%
8127
 
2.6%
9027
 
2.6%
7226
 
2.5%
8825
 
2.4%
6525
 
2.4%
Other values (53)753
72.9%
ValueCountFrequency (%)
201
 
0.1%
251
 
0.1%
406
0.6%
416
0.6%
4214
1.4%
433
 
0.3%
448
0.8%
4512
1.2%
467
0.7%
4710
1.0%
ValueCountFrequency (%)
10015
1.5%
9915
1.5%
9820
1.9%
9715
1.5%
9627
2.6%
9529
2.8%
9425
2.4%
9313
1.3%
9231
3.0%
9116
1.5%

GPA year 1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)5.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.71414729
Minimum30
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:14.358781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q146
median58
Q371
95-th percentile82
Maximum85
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.24820418
Coefficient of variation (CV)0.2426707164
Kurtosis-1.117789841
Mean58.71414729
Median Absolute Deviation (MAD)12
Skewness0.1441683938
Sum60593
Variance203.0113225
MonotonicityNot monotonic
2022-08-04T15:42:14.559897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4138
 
3.7%
4434
 
3.3%
4334
 
3.3%
4534
 
3.3%
4731
 
3.0%
4629
 
2.8%
8029
 
2.8%
4228
 
2.7%
4027
 
2.6%
5026
 
2.5%
Other values (46)722
69.9%
ValueCountFrequency (%)
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
355
0.5%
366
0.6%
372
 
0.2%
386
0.6%
396
0.6%
ValueCountFrequency (%)
8519
1.8%
8417
1.6%
8312
1.2%
8213
1.3%
818
 
0.8%
8029
2.8%
7917
1.6%
7813
1.3%
7724
2.3%
7620
1.9%

GPA year 2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.93320426
Minimum0
Maximum87
Zeros112
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:14.777980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143
median58
Q372
95-th percentile83
Maximum87
Range87
Interquartile range (IQR)29

Descriptive statistics

Standard deviation23.69943395
Coefficient of variation (CV)0.4394219531
Kurtosis0.3619090467
Mean53.93320426
Median Absolute Deviation (MAD)15
Skewness-0.9754745629
Sum55713
Variance561.6631697
MonotonicityNot monotonic
2022-08-04T15:42:14.967916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0112
 
10.8%
7927
 
2.6%
4027
 
2.6%
5525
 
2.4%
7125
 
2.4%
4923
 
2.2%
8123
 
2.2%
6222
 
2.1%
4622
 
2.1%
8322
 
2.1%
Other values (48)705
68.2%
ValueCountFrequency (%)
0112
10.8%
303
 
0.3%
316
 
0.6%
3211
 
1.1%
339
 
0.9%
346
 
0.6%
3514
 
1.4%
367
 
0.7%
375
 
0.5%
3812
 
1.2%
ValueCountFrequency (%)
8712
1.2%
8515
1.5%
8414
1.4%
8322
2.1%
8218
1.7%
8123
2.2%
8016
1.5%
7927
2.6%
7819
1.8%
7718
1.7%

GPA year 3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.23233301
Minimum0
Maximum85
Zeros276
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:15.168816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49
Q367
95-th percentile82
Maximum85
Range85
Interquartile range (IQR)67

Descriptive statistics

Standard deviation29.01446846
Coefficient of variation (CV)0.6711289083
Kurtosis-1.178751671
Mean43.23233301
Median Absolute Deviation (MAD)19
Skewness-0.4536500729
Sum44659
Variance841.8393799
MonotonicityNot monotonic
2022-08-04T15:42:15.353107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0276
26.7%
4325
 
2.4%
4124
 
2.3%
4423
 
2.2%
5222
 
2.1%
6320
 
1.9%
4520
 
1.9%
6519
 
1.8%
7619
 
1.8%
6619
 
1.8%
Other values (47)566
54.8%
ValueCountFrequency (%)
0276
26.7%
302
 
0.2%
316
 
0.6%
322
 
0.2%
335
 
0.5%
345
 
0.5%
357
 
0.7%
3610
 
1.0%
3712
 
1.2%
384
 
0.4%
ValueCountFrequency (%)
8513
1.3%
8415
1.5%
8310
1.0%
8217
1.6%
816
 
0.6%
8017
1.6%
7913
1.3%
7812
1.2%
7712
1.2%
7619
1.8%

Overall GPA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct166
Distinct (%)16.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.11660207
Minimum20.5
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:15.550821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.5
5-th percentile38
Q151
median59.33333333
Q366.33333333
95-th percentile74.66666667
Maximum84
Range63.5
Interquartile range (IQR)15.33333333

Descriptive statistics

Standard deviation11.2569827
Coefficient of variation (CV)0.1936965049
Kurtosis-0.2948603498
Mean58.11660207
Median Absolute Deviation (MAD)7.333333333
Skewness-0.4223969715
Sum59976.33333
Variance126.7196594
MonotonicityNot monotonic
2022-08-04T15:42:15.752935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3333333324
 
2.3%
58.3333333319
 
1.8%
67.3333333317
 
1.6%
6017
 
1.6%
6716
 
1.5%
6416
 
1.5%
60.6666666716
 
1.5%
5715
 
1.5%
6115
 
1.5%
56.3333333315
 
1.5%
Other values (156)862
83.4%
ValueCountFrequency (%)
20.51
 
0.1%
27.333333331
 
0.1%
27.666666671
 
0.1%
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
356
0.6%
368
0.8%
ValueCountFrequency (%)
841
 
0.1%
82.666666671
 
0.1%
822
0.2%
812
0.2%
80.666666671
 
0.1%
80.51
 
0.1%
80.333333332
0.2%
803
0.3%
793
0.3%
78.51
 
0.1%

Progress
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size2.1 KiB
True
848 
False
184 
(Missing)
 
1
ValueCountFrequency (%)
True848
82.1%
False184
 
17.8%
(Missing)1
 
0.1%
2022-08-04T15:42:15.970974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

First Sit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.016472868
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:16.080561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.303847906
Coefficient of variation (CV)0.3246250998
Kurtosis-0.7057159947
Mean4.016472868
Median Absolute Deviation (MAD)1
Skewness0.02471814333
Sum4145
Variance1.700019361
MonotonicityNot monotonic
2022-08-04T15:42:16.216803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3373
36.1%
4218
21.1%
6189
18.3%
5183
17.7%
236
 
3.5%
133
 
3.2%
(Missing)1
 
0.1%
ValueCountFrequency (%)
133
 
3.2%
236
 
3.5%
3373
36.1%
4218
21.1%
5183
17.7%
6189
18.3%
ValueCountFrequency (%)
6189
18.3%
5183
17.7%
4218
21.1%
3373
36.1%
236
 
3.5%
133
 
3.2%

Second Sit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing8
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.834146341
Minimum0
Maximum5
Zeros208
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:16.347781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.251485398
Coefficient of variation (CV)0.6823258154
Kurtosis-0.8062111839
Mean1.834146341
Median Absolute Deviation (MAD)1
Skewness-0.01364452657
Sum1880
Variance1.566215701
MonotonicityNot monotonic
2022-08-04T15:42:16.481835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3349
33.8%
2231
22.4%
0208
20.1%
1199
19.3%
520
 
1.9%
418
 
1.7%
(Missing)8
 
0.8%
ValueCountFrequency (%)
0208
20.1%
1199
19.3%
2231
22.4%
3349
33.8%
418
 
1.7%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
418
 
1.7%
3349
33.8%
2231
22.4%
1199
19.3%
0208
20.1%

Fails
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.5639534884
Minimum0
Maximum5
Zeros848
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:16.611854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.309437159
Coefficient of variation (CV)2.321888571
Kurtosis3.595023888
Mean0.5639534884
Median Absolute Deviation (MAD)0
Skewness2.20925508
Sum582
Variance1.714625674
MonotonicityNot monotonic
2022-08-04T15:42:16.752197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0848
82.1%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
110
 
1.0%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0848
82.1%
110
 
1.0%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
ValueCountFrequency (%)
532
 
3.1%
439
 
3.8%
350
 
4.8%
253
 
5.1%
110
 
1.0%
0848
82.1%

No Submissions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.234496124
Minimum0
Maximum5
Zeros423
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:16.892052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.364384136
Coefficient of variation (CV)1.105215407
Kurtosis-0.06991856543
Mean1.234496124
Median Absolute Deviation (MAD)1
Skewness0.9483452924
Sum1274
Variance1.861544072
MonotonicityNot monotonic
2022-08-04T15:42:17.030822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
476
 
7.4%
396
 
9.3%
2165
 
16.0%
1252
24.4%
0423
40.9%

Late Submission
Categorical

Distinct4
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size8.2 KiB
1.0
423 
0.0
409 
2.0
175 
3.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3096
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0423
40.9%
0.0409
39.6%
2.0175
16.9%
3.025
 
2.4%
(Missing)1
 
0.1%

Length

2022-08-04T15:42:17.184606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-04T15:42:17.513887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0423
41.0%
0.0409
39.6%
2.0175
17.0%
3.025
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2064
66.7%
Other Punctuation1032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01441
69.8%
1423
 
20.5%
2175
 
8.5%
325
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.1032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Pass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean91.62080103
Minimum16.66666667
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-04T15:42:17.655771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.66666667
5-th percentile33.33333333
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range83.33333333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.83237346
Coefficient of variation (CV)0.216461472
Kurtosis3.835663087
Mean91.62080103
Median Absolute Deviation (MAD)0
Skewness-2.272267351
Sum94552.66667
Variance393.3230371
MonotonicityNot monotonic
2022-08-04T15:42:17.781981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
100848
82.1%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
16.6666666710
 
1.0%
861
 
0.1%
(Missing)1
 
0.1%
ValueCountFrequency (%)
16.6666666710
 
1.0%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
861
 
0.1%
100848
82.1%
ValueCountFrequency (%)
100848
82.1%
861
 
0.1%
83.3333333332
 
3.1%
66.6666666739
 
3.8%
5050
 
4.8%
33.3333333352
 
5.0%
16.6666666710
 
1.0%

Re Takes
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size2.1 KiB
False
878 
True
154 
(Missing)
 
1
ValueCountFrequency (%)
False878
85.0%
True154
 
14.9%
(Missing)1
 
0.1%
2022-08-04T15:42:17.928850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2022-08-04T15:42:03.306202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.092362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:38.344033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.764957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.035148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:45.553154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.670294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.808127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.096087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.266397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:56.583808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.858408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:01.011763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:03.467916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.282971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:38.512396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.947912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.224888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:45.722157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.849105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.972106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.264923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.432883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:56.758800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.031850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:01.184130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:03.635848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.456122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:38.695164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.126841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.419973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:45.892459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.031898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:50.153790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.440840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.606420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:56.984917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.213254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:01.354838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:03.806891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.624392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:38.882015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.301929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.606803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.065274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.216817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:50.319891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.619220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.784956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:57.173944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.395092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:01.524899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:03.986790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.821737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:39.078847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.489467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.809136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.238000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.387881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:50.494368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.801049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.964913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:57.353000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.566900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:01.873073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.168132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:36.991899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:39.251004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.659962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:43.986744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.397895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.549831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:50.837062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:52.970197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.127278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:57.558856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.729426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.031763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.314835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.165095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:39.416070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.826984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:44.158273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.552237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.709974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:50.992169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.131885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.279326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:57.729946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:59.879877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.187404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.498814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.326290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:39.580994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:41.991743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:44.322783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.710763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:48.864001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.148871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.285920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.434836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:57.887027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.032979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.341862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.659890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.497955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:39.917907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:42.176348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:44.534765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:46.877707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.026064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.307925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.453398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.596928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.055790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.202025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.508040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.819836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.661904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.087842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:42.348196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:44.713137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.035830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.192259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.465952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.617776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.763964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.221275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.368223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.668843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:04.980839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.825011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.259964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:42.520049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:44.881837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.198124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.348815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.624907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.785815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:55.927813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.380891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.526814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.833898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:05.176842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:37.991827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.427208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:42.693835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:45.051802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.357821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.501836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.784066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:53.950970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:56.265955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.537005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.699211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:02.991274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:05.335810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:38.180948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:40.599884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:42.867955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:45.389576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:47.517407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:49.655029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:51.946820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:54.115173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:56.431125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:41:58.699826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:00.858775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-04T15:42:03.157962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-04T15:42:18.058910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-04T15:42:18.376005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-04T15:42:18.679879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-04T15:42:19.010127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-04T15:42:19.466415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-04T15:42:05.696765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-04T15:42:07.210274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-04T15:42:08.045903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-04T15:42:08.580469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CourseUCAS25 AboveDisabilityesEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudenot VisaRefugeeLonodono Permanoenot ResidenoceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceGPA year 1GPA year 2GPA year 3Overall GPAProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPassRe Takes
0BA Business Manangement Enterpreneurship and InnovationnononoAsian BritishMalenononoyes4.0nonoyesnoyes98.05.04.0yesnoyesno86.085.0584362.000000yes3.03.00.02.02.0100.000000yes
1BA Business ManagementnononoWhiteMaleyesnonoyes2.0yesnononono101.05.05.0yesnoyesyes55.040.032036.000000no1.02.05.03.00.083.333333no
2BA Business Management Enterpreneurship and InnovationnononoAsian British PakistaniMaleyesyesyesyes4.0yesnononoyes129.04.04.0yesnoyesno57.041.00041.000000yes6.00.00.00.00.0100.000000no
3BA Business ManagementnoyesnoWhiteFemaleyesnonono3.0yesnononoyes110.09.08.0yesnoyesno48.041.043042.000000yes6.00.00.00.00.0100.000000no
4BA Business Management Enterpreneurship and InnovationnononoAsian/Asian BritishMalenoyesyesyes4.0yesnononoyes130.06.05.0yesnoyesno83.055.0495954.333333yes4.02.00.02.00.0100.000000no
5BA Business Management Enterpreneurship and InnovationyesnonoAsian/Asian BritishMalenoyesyesyes3.0yesnononoyes112.06.04.0noyesnono71.046.0464345.000000yes3.03.00.00.01.0100.000000no
6BA Business Management MarketingyesnonoWhiteMalenonoyesno5.0nonoyesnono89.06.05.0yesnonono96.078.0707975.666667yes4.02.00.00.02.0100.000000no
7BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno4.0yesnononoyes103.04.05.0yesnonono67.043.0856163.000000yes3.03.00.03.00.0100.000000no
8BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno2.0nonononoyes128.04.04.0noyesnoyes89.076.0584459.333333yes6.00.00.00.00.0100.000000no
9BA Business ManagementyesnonoWhiteFemalenoyesyesno2.0nonononono91.04.04.0nononono92.049.0836766.333333yes6.00.00.01.01.0100.000000no

Last rows

CourseUCAS25 AboveDisabilityesEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudenot VisaRefugeeLonodono Permanoenot ResidenoceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceGPA year 1GPA year 2GPA year 3Overall GPAProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPassRe Takes
1023BAyesnonoAsianMalenononoyes5.0yesnononono107.06.07.0noyesnono96.080.00080.0yes6.00.00.00.01.0100.000000no
1024BAyesyesnoWhiteMalenoyesyesno3.0yesnononono103.05.06.0noyesyesno67.040.00040.0yes1.05.00.03.00.0100.000000no
1025BAyesnonoUnknownMalenoyesyesyes4.0yesnononono100.05.04.0yesnoyesno70.053.00053.0yes6.00.00.00.01.0100.000000no
1026BAyesyesnoUnknownFemalenononoyes3.0yesnononono113.03.06.0noyesnono64.073.00073.0yes3.03.00.02.01.0100.000000no
1027BAyesyesnoUnknownMalenoyesnoyes2.0yesnononoyes118.05.05.0yesnoyesyes96.068.00068.0yes3.03.00.01.00.0100.000000no
1028BAyesnonoOtherFemaleyesnoyesno2.0yesnononono102.04.04.0yesnoyesno55.045.00045.0yes6.00.00.00.01.0100.000000no
1029BAyesnonoUnknownMalenoyesyesyes4.0yesnononoyes109.04.04.0yesnoyesno66.077.00077.0yes6.00.00.00.00.0100.000000no
1030BAnoyesnoOther Asian BackgroundFemaleyesnonono1.0nonononono104.06.05.0yesnoyesno42.033.00033.0no1.01.02.04.01.033.333333yes
1031BAnoyesnoOtherMalenononoyes4.0yesnononono101.06.06.0noyesnono60.076.00076.0yes6.00.00.00.00.0100.000000no
1032BAnoyesnoOther ethnic backgroundFemalenonononoNaNnonoyesnono104.08.04.0nononono71.080.00080.0yes6.00.00.00.00.0100.000000no